Risk Assessment and Spatial Modeling of Heavy Metals Contamination in Topsoil around Venarj Manganese Mine by Artificial Neural Networks Method

Background and Objectives: The aim of the present study was to assess the probable heavy metals contamination in topsoil surrounding Venarj mine in Qom province using contamination indices and artificial neural networks method. Material and methods:  in order to evaluate the contamination status around Venarj mine in Qom province, 70 soil samples were collected in an area of 22 Km2, and  the total average concentration of Mn, Pb, Zn, Cu and Ni was measured. The indices of geoaccumulation (Igeo), contamination factor (CF), pollution load index and human health risk assessment index were applied to quantify the risk of soil contamination with heavy metals. Also multilayer perceptron (MLP) neural networks method was used to determine the spatial pattern of soil contamination with heavy metals. Results: Applying contamination indices indicated that the soil of the Venarj area was contaminated with Mn and Pb and to some extent with Cu due to mining activities. However, no significant enrichment of Zn and Ni was observed. Human health risk assessment indicated that the highest risk of carcinogenic and non- carcinogenic were associated with Pb, where carcinogenic risk was higher in children than adults. Spatial distribution patterns of the elements demonstrated that the maximum concentration of heavy metals was observed in western (extraction region) and southwestern (crusher region) areas. Distance increasing from mine decreased the concentration of all metals in topsoil samples, indicating their mining source. Conclusion: The results revealed that the topsoil concentration of Mn and Cu and especially Pb around the Venarj manganese mine was increased due to the mining activities. The most negative environmental and health effects of the mine resulted from higher concentration of Pb in the soil.